This paper introduces a compact, affordable electronic nose (e-nose) device devoted to detect the presence of toxic compounds that could affect human health, such as carbon monoxide, combustible gas, hydrogen, methane, and smoke, among others. Such artificial olfaction device consists of an array of six metal oxide semiconductor (MOS) sensors and a computer-based information system for signal acquisition, processing, and visualization. This study further proposes the use of the filter diagonalization method (FDM) to extract the spectral contents of the signals obtained from the sensors. Preliminary results show that the prototype is functional and that the FDM approach is suitable for a later classification stage. Example deployment scenarios of the proposed e-nose include indoor facilities (buildings and warehouses), compromised air quality places (mines and sanitary landfills), public transportation, mobile robots, and wireless sensor networks.
The electric bicycle is considered as an environmentally friendly mode, the market share of which is growing fast worldwide. Even in metropolitan areas which have a well-developed public transportation system, the usage of electric bicycles continues to grow. Compared with bicycles, the power transferred from the battery enables users to ride faster and have long-distance trips. However, research on electric bicycle travel behavior is inadequate. This paper proposes a cumulative prospect theory (CPT) framework to describe electric bicycle users’ mode choice behavior. Different from the long-standing use of utility theory, CPT considers travelers’ inconsistent risk attitudes. Six socioeconomic characteristics are chosen to discriminate conservative and adventurous electric bicycle users. Then, a CPT model is established which includes two parts: travel time and travel cost. We calculate the comprehensive cumulative prospect value (CPV) for four transportation modes (electric bicycle, bus, subway and private car) to predict electric bicycle users’ mode choice preference under different travel distance ranges. The model is further validated via survey data.
Purpose: this study aims to examine the community's decision to migrate between regions in the Jabodetabek area using the KRL Commuterline public transportation and analyse regional criteria based on regional development based on Oriented Development Transit, where these criteria become integration with community movements in migrating to an area.Methods: secondary data is used to fnd the number of people in migrating obtained from pt. Kai Indonesia. While to complete and explain each variable to be studied using primary data with several questions through a questionnaire submitted to 398 people who migrate between regions using logistic regression analysis techniques in their measurements. While to analyze the criteria for regional development in each region using an assessment approach from the Institute for Transportation and Development Policy. With qualitative analysis techniques and to assist in this research, a spatial approach is used which is used to display a picture of the distribution of migration.Results: (1) Regional development in each part of the Jabodetabek area is in the silver standard category which indicates that the regional development project has almost met the performance targets that have been conceptualized by the Institute for Transportation and Development Policy. (2) People in making decisions to migrate between regions will be aﬀected by the variables of distance, travel costs, gender, travel time, migration destination and regional development, while age and transit distance cannot provide a large enough inﬂuence on people's movements in migrating.Conclusions and Relevance: the results of the study prove that regional development in the Jabodetabek area tends to be a non-metropolitan area where people who move prefer areas that are integrated with public facilities that lead to the destination rather than towards the metropolitan area, this is evidenced by the standard silver criteria obtained in the area in Jabodetabek.
Objectives: The COVID-19 pandemic undermined the health service delivery and utilization of essential health care services globally. The current study therefore aimed to explore the health-seeking behaviors and challenges faced by patients for the management of gastrointestinal diseases.
Methods: A cross-sectional study was conducted at the outpatient department of Gastroenterology, Liaquat National Hospital, Karachi from March 2020 to July 2020 during the COVID-19 lockdown phase to explore patient experiences. Data was collected using a survey questionnaire. All patients of either gender were included after informed consent. Statistical analysis of the data was conducted using SPSS 21.0.
Results: A total of 184 patients were included who visited the hospital to seek medical services during the COVID-19 lockdown phase. The mean age of the population was 42.7 years (±16.13). Of these, n=94 (51.1%) were males All patients had gastrointestinal issues with different comorbid conditions. One forty-seven n=147 (79.9%) presented with active complaints whereas, n=37 (20.1%) patients visited the hospital for their follow-up checkup. Out of 184 patients, n=33 (17.9%) patients reported of having fear of visiting hospital due to COVID-19 outbreak. A statistically significant difference p<0.001 was noted between the history of comorbidities and patient delaying a visit to the healthcare due to the fear of COVID-19. Additionally, 61 (73.5%) patients with co-morbidity faced difficulty in finding public transport (p=0.01). Nevertheless, n=171 (93.0%) patients expressed satisfaction with the services provided by the hospital during the lockdown phase.
Conclusion: Patients with gastrointestinal conditions were largely affected by lockdown largely due to fear of contacting COVID-19 disease and inaccessibility to the public transportation. Widely available telemedicine service might overcome these shortcomings and ensure continuity of quality care.
How to cite this:Kalwar HA, Kamani L. Problems faced by patients and health service utilization experiences of gastrointestinal patients during lockdown due to COVID-19 pandemic. Pak J Med Sci. 2022;38(3):---------. doi: https://doi.org/10.12669/pjms.38.3.4799
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
With people restricted to their residences, neighbourhood characteristics may affect behaviour and risk of coronavirus disease 2019 (COVID-19) infection. We aimed to analyse whether neighbourhoods with higher walkability, public transit, biking services and higher socio-economic status were associated with lower COVID-19 infection during the peak of the COVID-19 pandemic in Massachusetts. We used Walk Score®, Bike Score®, and Transit Score® indices to assess the walkability and transportation of 72 cities in Massachusetts, USA based on availability of data and collected the total COVID-19 case numbers of each city up to 10 April 2021. We used univariate and multivariate linear models to analyse the effects of these scores on COVID-19 cases per 100,000 in each city, adjusting for demographic covariates and all covariates, respectively. In the 72 cities studied, the average Walk Score, Transit Score and Bike Score was 48.7, 36.5 and 44.1, respectively, with a total of 426,182 COVID-19 cases. Higher Walk Score, Transit Score, and Bike Score rankings were negatively associated with COVID-19 cases per 100,000 persons (<0.05). Cities with a higher proportion of Hispanic population and a lower median household income were associated with more COVID-19 cases per 100,000 (P<0.05). Higher Walk Score, Transit Score and Bike Score were shown to be protective against COVID-19 transmission, while socio-demographic factors were associated with COVID-19 infection. Understanding the complex relationship of how the structure of the urban environment may constrain commuting patterns for residents and essential workers during COVID-19 would offer potential insights on future pandemic preparedness and response.
Improving travel time prediction for public transit effectively enhances service reliability, optimizes travel structure, and alleviates traffic problems. Its greater time-variance and uncertainty make predictions for short travel times (≤35min) more subject to be influenced by random factors. It requires higher precision and is more complicated than long-term predictions. Effectively extracting and mining real-time, accurate, reliable, and low-cost multi-source data such as GPS, AFC, and IC can provide data support for travel time prediction. Kalman filter model has high accuracy in one-step prediction and can be used to calculate a large amount of data. This paper adopts the Kalman filter as a travel time prediction model for a single bus based on single-line detection: including the travel time prediction model of route (RTM) and the stop dwell time prediction model (DTM); the evaluation criteria and indexes of the models are given. The error analysis of the prediction results is carried out based on AVL data by case study. Results show that under the precondition of multi-source data, the public transportation prediction model can meet the accuracy requirement for travel time prediction and the prediction effect of the whole route is superior to that of the route segment between stops.
Shared mobility is a viable choice to improve the connectivity of lower-density neighbourhoods or suburbs that lack high-frequency public transportation services. In addition, its integration with new forms of powertrain and autonomous technologies can achieve more sustainable and efficient transportation. This study compares four shared-mobility technologies in suburban areas: the Internal Combustion Engine, Battery Electric, and two Autonomous Electric Vehicle scenarios, for various passenger capacities ranging from three to fifteen. The study aims to provide policymakers, transportation planners, and transit providers with insights into the potential costs and benefits as well as system configurations of shared mobility in a suburban context. A vehicle routing problem with time windows was applied using the J-Horizon software to optimize the costs of serving existing intra-community demand. The results indicate a similar fleet composition for Battery Electric and Autonomous Electric fleets. Furthermore, the resulting fleet for all four technologies is dominated by larger vehicle capacities. Due to the large share of driver cost in the total cost, the savings using a fleet of Autonomous Electric Vehicles are predicted to be 68% and 70%, respectively, compared to Internal Combustion and Battery Electric fleets.
Background: This study aimed to determine whether people living in the Eastern Region of Saudi Arabia would prefer to continue the practice of physical distancing after the coronavirus disease 2019 (COVID-19) pandemic or to return to their previous way of life. Methods: This cross-sectional study was conducted from August 2020 to October 2020 in the Eastern Region of Saudi Arabia. A pre-tested questionnaire was sent electronically through social media. Data on participants’ demographics and their perspectives regarding post-pandemic physical distancing were collected. The calculated sample size was 1,066; however, the total number of responses included in the analysis was 989. Results: The average age of the participants was 31.15±11.93 years. There were 435 men and 554 women in the study. Participants showed significantly high levels of disagreement with statements indicating that they were willing to use public transportation (61%), attend social gatherings (36%), and hug relatives or colleagues (40%) after the pandemic (p<0.001); however, 43% agreed that they would spend time with family or friends (p<0.001). The level of education was also found to be significantly related to the responses, and the level of disagreement increased as the level of education increased (p<0.001). Conclusions: One-third of the study participants planned to continue engaging in physical distancing even after the current pandemic and if the COVID-19 related restriction will remain in place for longer, there is chance to increase in this proportion. However, it cannot be concluded whether or not this behavior will prevail in the long run, after the ease in restrictions. If so, it may greatly affect some businesses and perhaps some social norms and values as well.
Currently, many people enjoy videos and music content through their smart devices while using public transportation. However, because passengers focus so much on content on their smart devices, they sometimes forget to disembark and miss their destination stations. Therefore, in this paper, we propose an application that can notify users via smart devices when they approach the drop-off point in public transportation using an inaudible high frequency. Inaudible frequency signals are generated with announcements from speakers installed on subways and city buses. Smart devices receive and analyze the signals through their built-in microphones and notify users when they reach the drop-off point. We tested destination notifications with the proposed system and 10 smart devices to evaluate its performance. According to the test results, the proposed system showed 99.4% accuracy on subways and 99.2% accuracy on city buses. Moreover, we compared these results to those using only subway app in subways, and our proposed system achieved far better outcomes. Thus, the proposed system could be a useful technology for notifying smart device users when to get off public transport, and it will become an innovative technology for global public transportation by informing users of their desired stations using speakers.